"""
Neural Architecture Optimizer

This module provides NAS engine with capability-based knowledge structure (CbKST),
multi-objective optimization balancing accuracy, efficiency, and historical fidelity,
model compression suite, and hardware acceleration framework integration.

Key Features:
- NAS engine with capability-based knowledge structure (CbKST)
- Multi-objective optimization balancing accuracy, efficiency, historical fidelity
- Model compression suite (knowledge distillation, pruning, quantization)
- Hardware acceleration framework integration
- Automated neural architecture search and optimization
- Performance benchmarking and model deployment
"""

import numpy as np
from typing import Dict, List, Tuple, Optional, Any, Callable, Union
from dataclasses import dataclass, field
from enum import Enum
import logging
from datetime import datetime, timedelta
from collections import defaultdict, deque
import json
import time
import random
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import threading
import pickle
import hashlib
from pathlib import Path

logger = logging.getLogger(__name__)


class OptimizationObjective(Enum):
    """Optimization objectives for neural architecture search."""
    ACCURACY = "accuracy"
    EFFICIENCY = "efficiency"
    HISTORICAL_FIDELITY = "historical_fidelity"
    MEMORY_USAGE = "memory_usage"
    INFERENCE_SPEED = "inference_speed"
    ENERGY_CONSUMPTION = "energy_consumption"
    MODEL_SIZE = "model_size"


class ArchitectureType(Enum):
    """Types of neural architectures."""
    TRANSFORMER = "transformer"
    CNN = "cnn"
    RNN = "rnn"
    HYBRID = "hybrid"
    HDC_BASED = "hdc_based"
    CUSTOM = "custom"


class CompressionMethod(Enum):
    """Model compression methods."""
    KNOWLEDGE_DISTILLATION = "knowledge_distillation"
    PRUNING = "pruning"
    QUANTIZATION = "quantization"
    LOW_RANK_DECOMPOSITION = "low_rank_decomposition"
    SPARSIFICATION = "sparsification"


class HardwareTarget(Enum):
    """Hardware acceleration targets."""
    CPU = "cpu"
    GPU = "gpu"
    TPU = "tpu"
    EDGE_DEVICE = "edge_device"
    MOBILE = "mobile"
    FPGA = "fpga"


@dataclass
class CapabilityKnowledgeStructure:
    """Capability-based Knowledge Structure Tree (CbKST) node."""
    capability_id: str
    capability_name: str
    parent_capability: Optional[str]
    child_capabilities: List[str]
    required_knowledge: List[str]
    performance_metrics: Dict[str, float]
    architectural_requirements: Dict[str, Any]
    optimization_constraints: Dict[str, Any]
    
    def is_leaf_capability(self) -> bool:
        """Check if this is a leaf capability node."""
        return len(self.child_capabilities) == 0
    
    def get_depth(self, capabilities_db: Dict[str, 'CapabilityKnowledgeStructure']) -> int:
        """Get depth of capability in the tree."""
        if not self.parent_capability:
            return 0
        
        if self.parent_capability in capabilities_db:
            return 1 + capabilities_db[self.parent_capability].get_depth(capabilities_db)
        
        return 1


@dataclass
class ArchitectureCandidate:
    """Neural architecture candidate."""
    architecture_id: str
    architecture_type: ArchitectureType
    structure_params: Dict[str, Any]
    capabilities: List[str]
    
    # Performance metrics
    accuracy_score: float = 0.0
    efficiency_score: float = 0.0
    historical_fidelity_score: float = 0.0
    
    # Resource metrics
    model_size_mb: float = 0.0
    inference_time_ms: float = 0.0
    memory_usage_mb: float = 0.0
    energy_consumption_mj: float = 0.0
    
    # Multi-objective score
    pareto_score: float = 0.0
    dominance_rank: int = 0
    
    # Metadata
    generation: int = 0
    parent_architectures: List[str] = field(default_factory=list)
    training_history: Dict[str, Any] = field(default_factory=dict)
    compression_applied: List[CompressionMethod] = field(default_factory=list)
    
    def calculate_pareto_score(self, weights: Dict[OptimizationObjective, float]) -> float:
        """Calculate Pareto score based on multiple objectives."""
        objective_values = {
            OptimizationObjective.ACCURACY: self.accuracy_score,
            OptimizationObjective.EFFICIENCY: self.efficiency_score,
            OptimizationObjective.HISTORICAL_FIDELITY: self.historical_fidelity_score,
            OptimizationObjective.MEMORY_USAGE: 1.0 - min(1.0, self.memory_usage_mb / 1000.0),  # Invert
            OptimizationObjective.INFERENCE_SPEED: 1.0 - min(1.0, self.inference_time_ms / 1000.0),  # Invert
            OptimizationObjective.MODEL_SIZE: 1.0 - min(1.0, self.model_size_mb / 100.0),  # Invert
            OptimizationObjective.ENERGY_CONSUMPTION: 1.0 - min(1.0, self.energy_consumption_mj / 10.0)  # Invert
        }
        
        weighted_score = 0.0
        total_weight = 0.0
        
        for objective, weight in weights.items():
            if objective in objective_values:
                weighted_score += weight * objective_values[objective]
                total_weight += weight
        
        self.pareto_score = weighted_score / total_weight if total_weight > 0 else 0.0
        return self.pareto_score


@dataclass
class CompressionResult:
    """Result of model compression operation."""
    original_architecture: str
    compressed_architecture: str
    compression_method: CompressionMethod
    compression_ratio: float  # Original size / Compressed size
    accuracy_retention: float  # Retained accuracy (0.0 to 1.0)
    speed_improvement: float  # Speed improvement factor
    memory_reduction: float  # Memory reduction factor
    compression_time: float
    success: bool
    details: Dict[str, Any] = field(default_factory=dict)


@dataclass
class OptimizationResult:
    """Result of neural architecture optimization."""
    optimization_id: str
    timestamp: datetime
    best_architecture: ArchitectureCandidate
    pareto_frontier: List[ArchitectureCandidate]
    optimization_objectives: List[OptimizationObjective]
    optimization_time: float
    generations_completed: int
    total_architectures_evaluated: int
    convergence_achieved: bool
    hardware_target: HardwareTarget
    compression_results: List[CompressionResult] = field(default_factory=list)
    deployment_ready: bool = False


class NeuralArchitectureOptimizer:
    """
    Neural Architecture Optimizer with capability-based knowledge structure.
    
    This system provides automated neural architecture search, multi-objective
    optimization, model compression, and hardware acceleration integration
    for the AI historical simulation platform.
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """
        Initialize neural architecture optimizer.
        
        Args:
            config: Configuration dictionary with optimization parameters
        """
        self.config = config or {}
        
        # Optimization configuration
        self.population_size = self.config.get('population_size', 50)
        self.max_generations = self.config.get('max_generations', 100)
        self.mutation_rate = self.config.get('mutation_rate', 0.1)
        self.crossover_rate = self.config.get('crossover_rate', 0.7)
        self.elitism_ratio = self.config.get('elitism_ratio', 0.1)
        
        # Multi-objective optimization weights
        self.default_weights = {
            OptimizationObjective.ACCURACY: 0.35,
            OptimizationObjective.EFFICIENCY: 0.25,
            OptimizationObjective.HISTORICAL_FIDELITY: 0.20,
            OptimizationObjective.MEMORY_USAGE: 0.10,
            OptimizationObjective.INFERENCE_SPEED: 0.10
        }
        
        # Capability-based knowledge structure
        self.capabilities_db: Dict[str, CapabilityKnowledgeStructure] = {}
        self._initialize_capability_structure()
        
        # Architecture database
        self.architecture_database: Dict[str, ArchitectureCandidate] = {}
        self.optimization_history: List[OptimizationResult] = []
        
        # Compression and acceleration
        self.compression_methods = {
            CompressionMethod.KNOWLEDGE_DISTILLATION: self._apply_knowledge_distillation,
            CompressionMethod.PRUNING: self._apply_pruning,
            CompressionMethod.QUANTIZATION: self._apply_quantization,
            CompressionMethod.LOW_RANK_DECOMPOSITION: self._apply_low_rank_decomposition,
            CompressionMethod.SPARSIFICATION: self._apply_sparsification
        }
        
        # Hardware acceleration mappings
        self.hardware_optimizations = {
            HardwareTarget.CPU: self._optimize_for_cpu,
            HardwareTarget.GPU: self._optimize_for_gpu,
            HardwareTarget.TPU: self._optimize_for_tpu,
            HardwareTarget.EDGE_DEVICE: self._optimize_for_edge,
            HardwareTarget.MOBILE: self._optimize_for_mobile,
            HardwareTarget.FPGA: self._optimize_for_fpga
        }
        
        # Parallel processing
        self.max_workers = self.config.get('max_workers', 4)
        self.process_pool = ProcessPoolExecutor(max_workers=self.max_workers)
        self.thread_pool = ThreadPoolExecutor(max_workers=self.max_workers)
        
        # Performance tracking
        self.optimization_stats = {
            'total_optimizations': 0,
            'successful_optimizations': 0,
            'average_optimization_time': 0.0,
            'best_architectures_found': 0,
            'compression_operations': 0
        }
        
        logger.info("NeuralArchitectureOptimizer initialized with population_size=%d, max_generations=%d",
                   self.population_size, self.max_generations)
    
    def _initialize_capability_structure(self):
        """Initialize capability-based knowledge structure tree."""
        # Root capabilities
        root_capabilities = [
            CapabilityKnowledgeStructure(
                capability_id='historical_simulation',
                capability_name='Historical Figure Simulation',
                parent_capability=None,
                child_capabilities=['personality_modeling', 'dialogue_generation', 'knowledge_retrieval'],
                required_knowledge=['historical_facts', 'personality_psychology', 'linguistics'],
                performance_metrics={'accuracy': 0.0, 'consistency': 0.0, 'authenticity': 0.0},
                architectural_requirements={'min_parameters': 1000000, 'context_length': 2048},
                optimization_constraints={'max_latency_ms': 500, 'max_memory_mb': 512}
            )
        ]
        
        # Personality modeling capabilities
        personality_capabilities = [
            CapabilityKnowledgeStructure(
                capability_id='personality_modeling',
                capability_name='Personality Modeling',
                parent_capability='historical_simulation',
                child_capabilities=['trait_encoding', 'behavior_prediction', 'emotional_modeling'],
                required_knowledge=['personality_psychology', 'trait_theory', 'behavioral_patterns'],
                performance_metrics={'consistency': 0.0, 'accuracy': 0.0, 'stability': 0.0},
                architectural_requirements={'vector_dimension': 10000, 'encoding_layers': 3},
                optimization_constraints={'consistency_threshold': 0.85}
            ),
            CapabilityKnowledgeStructure(
                capability_id='trait_encoding',
                capability_name='Personality Trait Encoding',
                parent_capability='personality_modeling',
                child_capabilities=[],
                required_knowledge=['big_five_model', 'trait_psychology'],
                performance_metrics={'encoding_accuracy': 0.0, 'reconstruction_fidelity': 0.0},
                architectural_requirements={'embedding_dimension': 512, 'attention_heads': 8},
                optimization_constraints={'reconstruction_threshold': 0.95}
            )
        ]
        
        # Dialogue generation capabilities
        dialogue_capabilities = [
            CapabilityKnowledgeStructure(
                capability_id='dialogue_generation',
                capability_name='Historical Dialogue Generation',
                parent_capability='historical_simulation',
                child_capabilities=['context_understanding', 'response_generation', 'style_adaptation'],
                required_knowledge=['historical_linguistics', 'dialogue_systems', 'contextual_understanding'],
                performance_metrics={'coherence': 0.0, 'historical_accuracy': 0.0, 'engagement': 0.0},
                architectural_requirements={'sequence_length': 1024, 'vocabulary_size': 50000},
                optimization_constraints={'response_time_ms': 200}
            )
        ]
        
        # Knowledge retrieval capabilities
        knowledge_capabilities = [
            CapabilityKnowledgeStructure(
                capability_id='knowledge_retrieval',
                capability_name='Historical Knowledge Retrieval',
                parent_capability='historical_simulation',
                child_capabilities=['fact_retrieval', 'context_matching', 'relevance_ranking'],
                required_knowledge=['historical_databases', 'information_retrieval', 'knowledge_graphs'],
                performance_metrics={'retrieval_accuracy': 0.0, 'relevance_score': 0.0, 'coverage': 0.0},
                architectural_requirements={'memory_bank_size': 1000000, 'retrieval_layers': 2},
                optimization_constraints={'retrieval_latency_ms': 100}
            )
        ]
        
        # Build capabilities database
        all_capabilities = (root_capabilities + personality_capabilities + 
                          dialogue_capabilities + knowledge_capabilities)
        
        for capability in all_capabilities:
            self.capabilities_db[capability.capability_id] = capability
        
        logger.info("Initialized capability-based knowledge structure with %d capabilities",
                   len(self.capabilities_db))
    
    def optimize_architecture(self,
                            target_capabilities: List[str],
                            optimization_objectives: List[OptimizationObjective],
                            hardware_target: HardwareTarget = HardwareTarget.GPU,
                            objective_weights: Optional[Dict[OptimizationObjective, float]] = None,
                            constraints: Optional[Dict[str, Any]] = None) -> OptimizationResult:
        """
        Optimize neural architecture for specified capabilities and objectives.
        
        Args:
            target_capabilities: List of required capability IDs
            optimization_objectives: List of optimization objectives
            hardware_target: Target hardware platform
            objective_weights: Custom weights for objectives
            constraints: Additional optimization constraints
            
        Returns:
            OptimizationResult with best architecture and Pareto frontier
        """
        optimization_id = self._generate_optimization_id()
        start_time = time.time()
        
        # Validate capabilities
        for capability_id in target_capabilities:
            if capability_id not in self.capabilities_db:
                raise ValueError(f"Unknown capability: {capability_id}")
        
        # Use default weights if not provided
        if objective_weights is None:
            objective_weights = self.default_weights
        
        constraints = constraints or {}
        
        logger.info("Starting architecture optimization %s for capabilities: %s",
                   optimization_id, target_capabilities)
        
        # Initialize population
        population = self._initialize_population(target_capabilities, hardware_target, constraints)
        
        # Track optimization progress
        best_architectures = []
        pareto_frontier = []
        generation = 0
        convergence_achieved = False
        
        # Evolution loop
        for generation in range(self.max_generations):
            logger.debug("Generation %d: evaluating %d architectures", generation, len(population))
            
            # Evaluate population in parallel
            evaluated_population = self._evaluate_population_parallel(
                population, target_capabilities, objective_weights
            )
            
            # Update Pareto frontier
            pareto_frontier = self._update_pareto_frontier(evaluated_population, pareto_frontier)
            
            # Track best architecture
            current_best = max(evaluated_population, key=lambda a: a.pareto_score)
            best_architectures.append(current_best)
            
            # Check convergence
            if self._check_convergence(best_architectures):
                convergence_achieved = True
                logger.info("Convergence achieved at generation %d", generation)
                break
            
            # Selection and reproduction
            selected_parents = self._selection(evaluated_population)
            offspring = self._reproduction(selected_parents, target_capabilities)
            population = self._replacement(evaluated_population, offspring)
        
        # Find best architecture
        best_architecture = max(pareto_frontier, key=lambda a: a.pareto_score)
        
        # Apply model compression if requested
        compression_results = []
        if constraints.get('apply_compression', False):
            compression_results = self._apply_model_compression(
                best_architecture, hardware_target, constraints
            )
        
        # Create optimization result
        optimization_time = time.time() - start_time
        
        result = OptimizationResult(
            optimization_id=optimization_id,
            timestamp=datetime.now(),
            best_architecture=best_architecture,
            pareto_frontier=pareto_frontier,
            optimization_objectives=optimization_objectives,
            optimization_time=optimization_time,
            generations_completed=generation + 1,
            total_architectures_evaluated=len(population) * (generation + 1),
            convergence_achieved=convergence_achieved,
            hardware_target=hardware_target,
            compression_results=compression_results,
            deployment_ready=self._check_deployment_readiness(best_architecture, constraints)
        )
        
        # Store results
        self.optimization_history.append(result)
        self._update_optimization_stats(result)
        
        logger.info("Architecture optimization completed: best_score=%.3f, time=%.2fs, generations=%d",
                   best_architecture.pareto_score, optimization_time, generation + 1)
        
        return result
    
    def _initialize_population(self, target_capabilities: List[str],
                             hardware_target: HardwareTarget,
                             constraints: Dict[str, Any]) -> List[ArchitectureCandidate]:
        """Initialize population of architecture candidates."""
        population = []
        
        # Generate diverse initial architectures
        for i in range(self.population_size):
            architecture = self._generate_random_architecture(
                target_capabilities, hardware_target, constraints, generation=0
            )
            population.append(architecture)
        
        logger.debug("Initialized population with %d architectures", len(population))
        return population
    
    def _generate_random_architecture(self, target_capabilities: List[str],
                                    hardware_target: HardwareTarget,
                                    constraints: Dict[str, Any],
                                    generation: int) -> ArchitectureCandidate:
        """Generate random architecture candidate."""
        architecture_id = self._generate_architecture_id()
        
        # Choose architecture type based on capabilities
        architecture_type = self._select_architecture_type(target_capabilities)
        
        # Generate structure parameters
        structure_params = self._generate_structure_parameters(
            architecture_type, target_capabilities, hardware_target, constraints
        )
        
        # Create architecture candidate
        architecture = ArchitectureCandidate(
            architecture_id=architecture_id,
            architecture_type=architecture_type,
            structure_params=structure_params,
            capabilities=target_capabilities,
            generation=generation
        )
        
        return architecture
    
    def _select_architecture_type(self, capabilities: List[str]) -> ArchitectureType:
        """Select architecture type based on required capabilities."""
        # Simple heuristic - in production would be more sophisticated
        capability_requirements = set()
        
        for capability_id in capabilities:
            if capability_id in self.capabilities_db:
                capability = self.capabilities_db[capability_id]
                arch_reqs = capability.architectural_requirements
                
                if 'sequence_modeling' in arch_reqs:
                    capability_requirements.add('sequence')
                if 'attention_mechanism' in arch_reqs:
                    capability_requirements.add('attention')
                if 'memory_access' in arch_reqs:
                    capability_requirements.add('memory')
                if 'vector_operations' in arch_reqs:
                    capability_requirements.add('hdc')
        
        # Select architecture type
        if 'hdc' in capability_requirements:
            return ArchitectureType.HDC_BASED
        elif 'attention' in capability_requirements and 'sequence' in capability_requirements:
            return ArchitectureType.TRANSFORMER
        elif 'sequence' in capability_requirements:
            return ArchitectureType.RNN
        elif len(capability_requirements) > 2:
            return ArchitectureType.HYBRID
        else:
            return random.choice(list(ArchitectureType))
    
    def _generate_structure_parameters(self, architecture_type: ArchitectureType,
                                     capabilities: List[str],
                                     hardware_target: HardwareTarget,
                                     constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Generate structure parameters for architecture."""
        params = {}
        
        if architecture_type == ArchitectureType.TRANSFORMER:
            params = {
                'num_layers': random.randint(6, 24),
                'hidden_size': random.choice([256, 512, 768, 1024]),
                'num_heads': random.choice([8, 12, 16]),
                'intermediate_size': random.choice([1024, 2048, 4096]),
                'max_position_embeddings': random.choice([512, 1024, 2048]),
                'dropout_rate': random.uniform(0.1, 0.3)
            }
        
        elif architecture_type == ArchitectureType.HDC_BASED:
            params = {
                'vector_dimension': random.choice([5000, 10000, 15000]),
                'binding_layers': random.randint(2, 6),
                'bundling_layers': random.randint(1, 4),
                'cleanup_threshold': random.uniform(0.7, 0.9),
                'permutation_count': random.randint(3, 10)
            }
        
        elif architecture_type == ArchitectureType.RNN:
            params = {
                'num_layers': random.randint(2, 8),
                'hidden_size': random.choice([128, 256, 512, 1024]),
                'cell_type': random.choice(['LSTM', 'GRU']),
                'bidirectional': random.choice([True, False]),
                'dropout_rate': random.uniform(0.1, 0.4)
            }
        
        elif architecture_type == ArchitectureType.CNN:
            params = {
                'num_layers': random.randint(3, 12),
                'filters': [random.choice([32, 64, 128, 256]) for _ in range(random.randint(3, 6))],
                'kernel_sizes': [random.choice([3, 5, 7]) for _ in range(random.randint(3, 6))],
                'pooling_type': random.choice(['max', 'avg']),
                'dropout_rate': random.uniform(0.1, 0.3)
            }
        
        else:  # HYBRID or CUSTOM
            # Combine elements from different architectures
            params = {
                'architecture_components': random.sample(['transformer', 'rnn', 'cnn', 'hdc'], 
                                                       random.randint(2, 3)),
                'component_weights': [random.uniform(0.2, 0.8) for _ in range(3)],
                'fusion_method': random.choice(['concatenation', 'attention', 'weighted_sum'])
            }
        
        # Apply hardware-specific constraints
        params = self._apply_hardware_constraints(params, hardware_target, constraints)
        
        return params
    
    def _apply_hardware_constraints(self, params: Dict[str, Any],
                                  hardware_target: HardwareTarget,
                                  constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Apply hardware-specific constraints to parameters."""
        if hardware_target == HardwareTarget.MOBILE or hardware_target == HardwareTarget.EDGE_DEVICE:
            # Reduce model size for mobile/edge devices
            if 'hidden_size' in params:
                params['hidden_size'] = min(params['hidden_size'], 512)
            if 'num_layers' in params:
                params['num_layers'] = min(params['num_layers'], 6)
            if 'vector_dimension' in params:
                params['vector_dimension'] = min(params['vector_dimension'], 5000)
        
        elif hardware_target == HardwareTarget.TPU:
            # Optimize for TPU batch processing
            if 'hidden_size' in params:
                # Ensure hidden size is divisible by 128 for TPU efficiency
                params['hidden_size'] = ((params['hidden_size'] + 127) // 128) * 128
        
        # Apply user constraints
        max_params = constraints.get('max_parameters')
        if max_params:
            estimated_params = self._estimate_parameter_count(params)
            if estimated_params > max_params:
                # Scale down parameters
                scale_factor = (max_params / estimated_params) ** 0.5
                for key in ['hidden_size', 'vector_dimension', 'intermediate_size']:
                    if key in params:
                        params[key] = int(params[key] * scale_factor)
        
        return params
    
    def _estimate_parameter_count(self, params: Dict[str, Any]) -> int:
        """Estimate parameter count from structure parameters."""
        # Simplified estimation - in production would be more accurate
        param_count = 0
        
        if 'hidden_size' in params and 'num_layers' in params:
            hidden_size = params['hidden_size']
            num_layers = params['num_layers']
            
            # Rough estimation for transformer-like architectures
            param_count += hidden_size * hidden_size * num_layers * 4  # Attention weights
            param_count += hidden_size * params.get('intermediate_size', hidden_size * 4) * num_layers * 2  # FFN
        
        elif 'vector_dimension' in params:
            # HDC-based architecture
            vector_dim = params['vector_dimension']
            param_count += vector_dim * params.get('binding_layers', 3) * 2
            param_count += vector_dim * params.get('bundling_layers', 2)
        
        return param_count
    
    def _evaluate_population_parallel(self, population: List[ArchitectureCandidate],
                                    target_capabilities: List[str],
                                    objective_weights: Dict[OptimizationObjective, float]) -> List[ArchitectureCandidate]:
        """Evaluate population in parallel."""
        futures = []
        
        # Submit evaluation tasks
        for architecture in population:
            future = self.thread_pool.submit(
                self._evaluate_architecture,
                architecture, target_capabilities, objective_weights
            )
            futures.append((future, architecture))
        
        # Collect results
        evaluated_population = []
        for future, architecture in futures:
            try:
                evaluated_architecture = future.result(timeout=60)  # 1 minute timeout
                evaluated_population.append(evaluated_architecture)
            except Exception as e:
                logger.warning("Architecture evaluation failed: %s", e)
                # Use original architecture with zero scores
                architecture.accuracy_score = 0.0
                architecture.efficiency_score = 0.0
                architecture.historical_fidelity_score = 0.0
                evaluated_population.append(architecture)
        
        return evaluated_population
    
    def _evaluate_architecture(self, architecture: ArchitectureCandidate,
                             target_capabilities: List[str],
                             objective_weights: Dict[OptimizationObjective, float]) -> ArchitectureCandidate:
        """Evaluate single architecture candidate."""
        # Simulate architecture evaluation - in production would involve actual training/testing
        
        # Capability-based evaluation
        capability_scores = []
        for capability_id in target_capabilities:
            if capability_id in self.capabilities_db:
                capability = self.capabilities_db[capability_id]
                capability_score = self._evaluate_capability_fitness(architecture, capability)
                capability_scores.append(capability_score)
        
        # Calculate performance metrics
        if capability_scores:
            architecture.accuracy_score = np.mean(capability_scores)
        else:
            architecture.accuracy_score = random.uniform(0.6, 0.95)  # Simulated
        
        # Efficiency evaluation
        architecture.efficiency_score = self._evaluate_efficiency(architecture)
        
        # Historical fidelity evaluation
        architecture.historical_fidelity_score = self._evaluate_historical_fidelity(
            architecture, target_capabilities
        )
        
        # Resource metrics
        architecture.model_size_mb = self._estimate_model_size(architecture)
        architecture.inference_time_ms = self._estimate_inference_time(architecture)
        architecture.memory_usage_mb = self._estimate_memory_usage(architecture)
        architecture.energy_consumption_mj = self._estimate_energy_consumption(architecture)
        
        # Calculate Pareto score
        architecture.calculate_pareto_score(objective_weights)
        
        return architecture
    
    def _evaluate_capability_fitness(self, architecture: ArchitectureCandidate,
                                   capability: CapabilityKnowledgeStructure) -> float:
        """Evaluate how well architecture fits capability requirements."""
        fitness_score = 1.0
        
        arch_params = architecture.structure_params
        requirements = capability.architectural_requirements
        
        # Check architectural requirements
        for req_name, req_value in requirements.items():
            if req_name in arch_params:
                arch_value = arch_params[req_name]
                
                if isinstance(req_value, (int, float)):
                    if req_name.startswith('min_'):
                        # Minimum requirement
                        if arch_value < req_value:
                            fitness_score *= (arch_value / req_value)
                    elif req_name.startswith('max_'):
                        # Maximum requirement
                        if arch_value > req_value:
                            fitness_score *= (req_value / arch_value)
                    else:
                        # Target value - penalize deviation
                        deviation = abs(arch_value - req_value) / req_value
                        fitness_score *= max(0.1, 1.0 - deviation)
        
        # Architecture type compatibility
        if architecture.architecture_type == ArchitectureType.HDC_BASED:
            if 'vector_operations' in capability.required_knowledge:
                fitness_score *= 1.2  # Bonus for good match
        elif architecture.architecture_type == ArchitectureType.TRANSFORMER:
            if 'attention_mechanism' in capability.required_knowledge:
                fitness_score *= 1.1  # Small bonus
        
        return max(0.0, min(1.0, fitness_score))
    
    def _evaluate_efficiency(self, architecture: ArchitectureCandidate) -> float:
        """Evaluate architecture efficiency."""
        # Simple efficiency metric based on parameter count and structure
        param_count = self._estimate_parameter_count(architecture.structure_params)
        
        # Normalize by typical parameter counts (millions)
        normalized_params = param_count / 1000000.0
        
        # Efficiency is inverse of parameter count (smaller models are more efficient)
        efficiency = 1.0 / (1.0 + normalized_params)
        
        # Architecture-specific adjustments
        if architecture.architecture_type == ArchitectureType.HDC_BASED:
            efficiency *= 1.3  # HDC is inherently efficient
        elif architecture.architecture_type == ArchitectureType.CNN:
            efficiency *= 1.1  # CNN is relatively efficient
        
        return max(0.0, min(1.0, efficiency))
    
    def _evaluate_historical_fidelity(self, architecture: ArchitectureCandidate,
                                    target_capabilities: List[str]) -> float:
        """Evaluate historical fidelity capability."""
        fidelity_score = 0.8  # Base fidelity score
        
        # Check for historical-specific capabilities
        historical_capabilities = [
            'historical_knowledge', 'temporal_consistency', 'cultural_accuracy'
        ]
        
        for capability_id in target_capabilities:
            if any(hist_cap in capability_id for hist_cap in historical_capabilities):
                fidelity_score *= 1.1  # Bonus for historical capabilities
        
        # Architecture type influence
        if architecture.architecture_type == ArchitectureType.TRANSFORMER:
            fidelity_score *= 1.05  # Good for historical context
        elif architecture.architecture_type == ArchitectureType.HYBRID:
            fidelity_score *= 1.02  # Modest improvement
        
        return max(0.0, min(1.0, fidelity_score))
    
    def _estimate_model_size(self, architecture: ArchitectureCandidate) -> float:
        """Estimate model size in MB."""
        param_count = self._estimate_parameter_count(architecture.structure_params)
        
        # Assume 4 bytes per parameter (float32)
        size_bytes = param_count * 4
        size_mb = size_bytes / (1024 * 1024)
        
        return size_mb
    
    def _estimate_inference_time(self, architecture: ArchitectureCandidate) -> float:
        """Estimate inference time in milliseconds."""
        # Simple estimation based on architecture complexity
        param_count = self._estimate_parameter_count(architecture.structure_params)
        
        # Base inference time
        base_time = 50.0  # 50ms base
        
        # Scale with parameter count
        complexity_factor = (param_count / 1000000.0) ** 0.7  # Sublinear scaling
        inference_time = base_time * (1.0 + complexity_factor)
        
        # Architecture-specific adjustments
        if architecture.architecture_type == ArchitectureType.HDC_BASED:
            inference_time *= 0.6  # HDC is fast
        elif architecture.architecture_type == ArchitectureType.TRANSFORMER:
            inference_time *= 1.2  # Transformer has attention overhead
        elif architecture.architecture_type == ArchitectureType.RNN:
            inference_time *= 1.5  # RNN is sequential
        
        return inference_time
    
    def _estimate_memory_usage(self, architecture: ArchitectureCandidate) -> float:
        """Estimate memory usage in MB."""
        model_size = self._estimate_model_size(architecture)
        
        # Memory usage includes model + activations + gradients (during training)
        # For inference, roughly 2x model size (model + activations)
        memory_usage = model_size * 2.0
        
        return memory_usage
    
    def _estimate_energy_consumption(self, architecture: ArchitectureCandidate) -> float:
        """Estimate energy consumption in millijoules."""
        # Simple estimation based on operations and inference time
        inference_time_s = self._estimate_inference_time(architecture) / 1000.0
        param_count = self._estimate_parameter_count(architecture.structure_params)
        
        # Energy roughly proportional to operations and time
        operations = param_count * 2  # Rough estimate of FLOPs
        energy_mj = (operations * inference_time_s) / 100000.0  # Simplified calculation
        
        return energy_mj
    
    def _update_pareto_frontier(self, population: List[ArchitectureCandidate],
                              current_frontier: List[ArchitectureCandidate]) -> List[ArchitectureCandidate]:
        """Update Pareto frontier with new population."""
        all_architectures = population + current_frontier
        
        # Calculate dominance relationships
        pareto_frontier = []
        
        for arch1 in all_architectures:
            is_dominated = False
            
            for arch2 in all_architectures:
                if arch1.architecture_id != arch2.architecture_id:
                    if self._dominates(arch2, arch1):
                        is_dominated = True
                        break
            
            if not is_dominated:
                # Check if already in frontier
                if not any(arch.architecture_id == arch1.architecture_id for arch in pareto_frontier):
                    pareto_frontier.append(arch1)
        
        # Limit frontier size
        if len(pareto_frontier) > 20:  # Keep top 20 architectures
            pareto_frontier.sort(key=lambda a: a.pareto_score, reverse=True)
            pareto_frontier = pareto_frontier[:20]
        
        return pareto_frontier
    
    def _dominates(self, arch1: ArchitectureCandidate, arch2: ArchitectureCandidate) -> bool:
        """Check if arch1 dominates arch2 in Pareto sense."""
        # Multi-objective dominance: arch1 dominates arch2 if it's better in at least one
        # objective and not worse in any objective
        
        objectives1 = [
            arch1.accuracy_score,
            arch1.efficiency_score,
            arch1.historical_fidelity_score
        ]
        
        objectives2 = [
            arch2.accuracy_score,
            arch2.efficiency_score,
            arch2.historical_fidelity_score
        ]
        
        better_in_at_least_one = False
        worse_in_any = False
        
        for obj1, obj2 in zip(objectives1, objectives2):
            if obj1 > obj2:
                better_in_at_least_one = True
            elif obj1 < obj2:
                worse_in_any = True
        
        return better_in_at_least_one and not worse_in_any
    
    def _check_convergence(self, best_architectures: List[ArchitectureCandidate],
                          window_size: int = 10, threshold: float = 0.01) -> bool:
        """Check if optimization has converged."""
        if len(best_architectures) < window_size:
            return False
        
        recent_scores = [arch.pareto_score for arch in best_architectures[-window_size:]]
        score_variance = np.var(recent_scores)
        
        return score_variance < threshold
    
    def _selection(self, population: List[ArchitectureCandidate]) -> List[ArchitectureCandidate]:
        """Select parents for reproduction."""
        # Tournament selection
        tournament_size = 3
        num_parents = int(len(population) * 0.5)  # Select half as parents
        
        parents = []
        for _ in range(num_parents):
            tournament = random.sample(population, tournament_size)
            winner = max(tournament, key=lambda a: a.pareto_score)
            parents.append(winner)
        
        return parents
    
    def _reproduction(self, parents: List[ArchitectureCandidate],
                     target_capabilities: List[str]) -> List[ArchitectureCandidate]:
        """Generate offspring through crossover and mutation."""
        offspring = []
        
        while len(offspring) < len(parents):
            # Select two parents
            parent1, parent2 = random.sample(parents, 2)
            
            # Crossover
            if random.random() < self.crossover_rate:
                child1, child2 = self._crossover(parent1, parent2, target_capabilities)
                offspring.extend([child1, child2])
            else:
                offspring.extend([parent1, parent2])
        
        # Mutation
        for i, child in enumerate(offspring):
            if random.random() < self.mutation_rate:
                offspring[i] = self._mutate(child, target_capabilities)
        
        return offspring[:len(parents)]  # Return same number as parents
    
    def _crossover(self, parent1: ArchitectureCandidate, parent2: ArchitectureCandidate,
                  target_capabilities: List[str]) -> Tuple[ArchitectureCandidate, ArchitectureCandidate]:
        """Perform crossover between two parent architectures."""
        # Simple parameter mixing crossover
        child1_params = {}
        child2_params = {}
        
        all_params = set(parent1.structure_params.keys()) | set(parent2.structure_params.keys())
        
        for param_name in all_params:
            if random.random() < 0.5:
                # Child1 gets param from parent1, child2 from parent2
                if param_name in parent1.structure_params:
                    child1_params[param_name] = parent1.structure_params[param_name]
                if param_name in parent2.structure_params:
                    child2_params[param_name] = parent2.structure_params[param_name]
            else:
                # Child1 gets param from parent2, child2 from parent1
                if param_name in parent2.structure_params:
                    child1_params[param_name] = parent2.structure_params[param_name]
                if param_name in parent1.structure_params:
                    child2_params[param_name] = parent1.structure_params[param_name]
        
        # Create child architectures
        child1 = ArchitectureCandidate(
            architecture_id=self._generate_architecture_id(),
            architecture_type=parent1.architecture_type,
            structure_params=child1_params,
            capabilities=target_capabilities,
            parent_architectures=[parent1.architecture_id, parent2.architecture_id]
        )
        
        child2 = ArchitectureCandidate(
            architecture_id=self._generate_architecture_id(),
            architecture_type=parent2.architecture_type,
            structure_params=child2_params,
            capabilities=target_capabilities,
            parent_architectures=[parent1.architecture_id, parent2.architecture_id]
        )
        
        return child1, child2
    
    def _mutate(self, architecture: ArchitectureCandidate,
               target_capabilities: List[str]) -> ArchitectureCandidate:
        """Mutate architecture parameters."""
        mutated_params = architecture.structure_params.copy()
        
        # Randomly mutate some parameters
        for param_name, param_value in mutated_params.items():
            if random.random() < 0.3:  # 30% chance to mutate each parameter
                if isinstance(param_value, int):
                    # Integer mutation
                    mutation_range = max(1, int(param_value * 0.2))  # 20% range
                    mutated_params[param_name] = param_value + random.randint(-mutation_range, mutation_range)
                    mutated_params[param_name] = max(1, mutated_params[param_name])  # Ensure positive
                
                elif isinstance(param_value, float):
                    # Float mutation
                    mutation_range = param_value * 0.2  # 20% range
                    mutated_params[param_name] = param_value + random.uniform(-mutation_range, mutation_range)
                    mutated_params[param_name] = max(0.01, mutated_params[param_name])  # Ensure positive
                
                elif isinstance(param_value, list):
                    # List mutation - randomly change one element
                    if param_value:
                        idx = random.randint(0, len(param_value) - 1)
                        if isinstance(param_value[idx], (int, float)):
                            if isinstance(param_value[idx], int):
                                mutated_params[param_name][idx] = random.randint(1, param_value[idx] * 2)
                            else:
                                mutated_params[param_name][idx] = random.uniform(0.01, param_value[idx] * 2)
        
        # Create mutated architecture
        mutated_architecture = ArchitectureCandidate(
            architecture_id=self._generate_architecture_id(),
            architecture_type=architecture.architecture_type,
            structure_params=mutated_params,
            capabilities=target_capabilities,
            parent_architectures=[architecture.architecture_id]
        )
        
        return mutated_architecture
    
    def _replacement(self, population: List[ArchitectureCandidate],
                    offspring: List[ArchitectureCandidate]) -> List[ArchitectureCandidate]:
        """Replace population with offspring using elitism."""
        # Combine population and offspring
        combined = population + offspring
        
        # Sort by Pareto score
        combined.sort(key=lambda a: a.pareto_score, reverse=True)
        
        # Keep top architectures (elitism)
        elite_count = int(len(population) * self.elitism_ratio)
        new_population = combined[:len(population)]
        
        return new_population
    
    # Model compression methods
    
    def _apply_model_compression(self, architecture: ArchitectureCandidate,
                               hardware_target: HardwareTarget,
                               constraints: Dict[str, Any]) -> List[CompressionResult]:
        """Apply model compression methods."""
        compression_results = []
        
        # Determine which compression methods to apply
        compression_methods = constraints.get('compression_methods', [
            CompressionMethod.PRUNING,
            CompressionMethod.QUANTIZATION
        ])
        
        for method in compression_methods:
            if method in self.compression_methods:
                try:
                    result = self.compression_methods[method](architecture, hardware_target, constraints)
                    compression_results.append(result)
                except Exception as e:
                    logger.error("Compression method %s failed: %s", method.value, e)
        
        return compression_results
    
    def _apply_knowledge_distillation(self, architecture: ArchitectureCandidate,
                                    hardware_target: HardwareTarget,
                                    constraints: Dict[str, Any]) -> CompressionResult:
        """Apply knowledge distillation compression."""
        start_time = time.time()
        
        # Simulate knowledge distillation
        original_size = self._estimate_model_size(architecture)
        
        # Create smaller student architecture
        student_params = architecture.structure_params.copy()
        
        # Reduce model size
        if 'hidden_size' in student_params:
            student_params['hidden_size'] = int(student_params['hidden_size'] * 0.7)
        if 'num_layers' in student_params:
            student_params['num_layers'] = max(1, int(student_params['num_layers'] * 0.8))
        if 'vector_dimension' in student_params:
            student_params['vector_dimension'] = int(student_params['vector_dimension'] * 0.8)
        
        compressed_size = self._estimate_model_size(
            ArchitectureCandidate('temp', architecture.architecture_type, student_params, [])
        )
        
        compression_ratio = original_size / compressed_size if compressed_size > 0 else 1.0
        
        # Simulate performance impact
        accuracy_retention = random.uniform(0.85, 0.95)  # Knowledge distillation typically retains 85-95% accuracy
        speed_improvement = compression_ratio * 0.8  # Some overhead from distillation
        memory_reduction = compression_ratio
        
        compression_time = time.time() - start_time
        
        return CompressionResult(
            original_architecture=architecture.architecture_id,
            compressed_architecture=f"{architecture.architecture_id}_distilled",
            compression_method=CompressionMethod.KNOWLEDGE_DISTILLATION,
            compression_ratio=compression_ratio,
            accuracy_retention=accuracy_retention,
            speed_improvement=speed_improvement,
            memory_reduction=memory_reduction,
            compression_time=compression_time,
            success=True,
            details={'student_params': student_params}
        )
    
    def _apply_pruning(self, architecture: ArchitectureCandidate,
                      hardware_target: HardwareTarget,
                      constraints: Dict[str, Any]) -> CompressionResult:
        """Apply neural network pruning."""
        start_time = time.time()
        
        # Simulate pruning
        pruning_ratio = constraints.get('pruning_ratio', 0.5)  # Remove 50% of parameters
        
        original_size = self._estimate_model_size(architecture)
        compressed_size = original_size * (1 - pruning_ratio)
        compression_ratio = original_size / compressed_size
        
        # Pruning performance impact
        accuracy_retention = 1.0 - (pruning_ratio * 0.2)  # Some accuracy loss
        speed_improvement = 1.0 + (pruning_ratio * 0.8)  # Speed improvement from sparsity
        memory_reduction = compression_ratio
        
        compression_time = time.time() - start_time
        
        return CompressionResult(
            original_architecture=architecture.architecture_id,
            compressed_architecture=f"{architecture.architecture_id}_pruned",
            compression_method=CompressionMethod.PRUNING,
            compression_ratio=compression_ratio,
            accuracy_retention=accuracy_retention,
            speed_improvement=speed_improvement,
            memory_reduction=memory_reduction,
            compression_time=compression_time,
            success=True,
            details={'pruning_ratio': pruning_ratio}
        )
    
    def _apply_quantization(self, architecture: ArchitectureCandidate,
                          hardware_target: HardwareTarget,
                          constraints: Dict[str, Any]) -> CompressionResult:
        """Apply quantization compression."""
        start_time = time.time()
        
        # Determine quantization bits based on hardware
        if hardware_target in [HardwareTarget.MOBILE, HardwareTarget.EDGE_DEVICE]:
            quantization_bits = 8  # INT8 for mobile
        else:
            quantization_bits = 16  # FP16 for other hardware
        
        # Calculate compression ratio (32-bit float to quantized)
        compression_ratio = 32.0 / quantization_bits
        
        original_size = self._estimate_model_size(architecture)
        compressed_size = original_size / compression_ratio
        
        # Quantization performance impact
        accuracy_retention = 0.98 if quantization_bits == 16 else 0.95  # Minimal loss for FP16, some for INT8
        speed_improvement = 1.2 if quantization_bits == 8 else 1.1  # Speed improvement from reduced precision
        memory_reduction = compression_ratio
        
        compression_time = time.time() - start_time
        
        return CompressionResult(
            original_architecture=architecture.architecture_id,
            compressed_architecture=f"{architecture.architecture_id}_quantized",
            compression_method=CompressionMethod.QUANTIZATION,
            compression_ratio=compression_ratio,
            accuracy_retention=accuracy_retention,
            speed_improvement=speed_improvement,
            memory_reduction=memory_reduction,
            compression_time=compression_time,
            success=True,
            details={'quantization_bits': quantization_bits}
        )
    
    def _apply_low_rank_decomposition(self, architecture: ArchitectureCandidate,
                                    hardware_target: HardwareTarget,
                                    constraints: Dict[str, Any]) -> CompressionResult:
        """Apply low-rank matrix decomposition."""
        start_time = time.time()
        
        # Simulate low-rank decomposition
        rank_ratio = constraints.get('rank_ratio', 0.5)  # Use 50% rank
        
        original_size = self._estimate_model_size(architecture)
        
        # Low-rank approximation reduces parameters quadratically
        compression_ratio = 1.0 / (rank_ratio ** 2)
        compressed_size = original_size / compression_ratio
        
        # Performance impact
        accuracy_retention = 0.85 + (rank_ratio * 0.1)  # Higher rank retains more accuracy
        speed_improvement = compression_ratio * 0.9  # Some overhead from decomposition
        memory_reduction = compression_ratio
        
        compression_time = time.time() - start_time
        
        return CompressionResult(
            original_architecture=architecture.architecture_id,
            compressed_architecture=f"{architecture.architecture_id}_low_rank",
            compression_method=CompressionMethod.LOW_RANK_DECOMPOSITION,
            compression_ratio=compression_ratio,
            accuracy_retention=accuracy_retention,
            speed_improvement=speed_improvement,
            memory_reduction=memory_reduction,
            compression_time=compression_time,
            success=True,
            details={'rank_ratio': rank_ratio}
        )
    
    def _apply_sparsification(self, architecture: ArchitectureCandidate,
                            hardware_target: HardwareTarget,
                            constraints: Dict[str, Any]) -> CompressionResult:
        """Apply network sparsification."""
        start_time = time.time()
        
        # Simulate sparsification
        sparsity_ratio = constraints.get('sparsity_ratio', 0.7)  # 70% sparse
        
        original_size = self._estimate_model_size(architecture)
        
        # Sparsity reduces effective model size
        compression_ratio = 1.0 / (1.0 - sparsity_ratio)
        compressed_size = original_size / compression_ratio
        
        # Performance impact
        accuracy_retention = 1.0 - (sparsity_ratio * 0.15)  # Some accuracy loss
        speed_improvement = 1.0 + (sparsity_ratio * 0.5)  # Speed improvement with sparse operations
        memory_reduction = compression_ratio
        
        compression_time = time.time() - start_time
        
        return CompressionResult(
            original_architecture=architecture.architecture_id,
            compressed_architecture=f"{architecture.architecture_id}_sparse",
            compression_method=CompressionMethod.SPARSIFICATION,
            compression_ratio=compression_ratio,
            accuracy_retention=accuracy_retention,
            speed_improvement=speed_improvement,
            memory_reduction=memory_reduction,
            compression_time=compression_time,
            success=True,
            details={'sparsity_ratio': sparsity_ratio}
        )
    
    # Hardware optimization methods
    
    def _optimize_for_cpu(self, architecture: ArchitectureCandidate) -> ArchitectureCandidate:
        """Optimize architecture for CPU deployment."""
        optimized_params = architecture.structure_params.copy()
        
        # CPU optimizations
        if 'batch_size' in optimized_params:
            optimized_params['batch_size'] = min(optimized_params['batch_size'], 8)  # Smaller batch for CPU
        
        # Add CPU-specific optimizations
        optimized_params['cpu_optimized'] = True
        optimized_params['use_mkldnn'] = True  # Intel MKL-DNN optimization
        
        optimized_architecture = ArchitectureCandidate(
            architecture_id=f"{architecture.architecture_id}_cpu_opt",
            architecture_type=architecture.architecture_type,
            structure_params=optimized_params,
            capabilities=architecture.capabilities,
            parent_architectures=[architecture.architecture_id]
        )
        
        return optimized_architecture
    
    def _optimize_for_gpu(self, architecture: ArchitectureCandidate) -> ArchitectureCandidate:
        """Optimize architecture for GPU deployment."""
        optimized_params = architecture.structure_params.copy()
        
        # GPU optimizations
        if 'batch_size' in optimized_params:
            optimized_params['batch_size'] = max(optimized_params['batch_size'], 32)  # Larger batch for GPU
        
        # Ensure dimensions are GPU-friendly
        if 'hidden_size' in optimized_params:
            # Round to multiples of 64 for better GPU utilization
            hidden_size = optimized_params['hidden_size']
            optimized_params['hidden_size'] = ((hidden_size + 63) // 64) * 64
        
        optimized_params['gpu_optimized'] = True
        optimized_params['use_cuda'] = True
        optimized_params['mixed_precision'] = True  # FP16 optimization
        
        optimized_architecture = ArchitectureCandidate(
            architecture_id=f"{architecture.architecture_id}_gpu_opt",
            architecture_type=architecture.architecture_type,
            structure_params=optimized_params,
            capabilities=architecture.capabilities,
            parent_architectures=[architecture.architecture_id]
        )
        
        return optimized_architecture
    
    def _optimize_for_tpu(self, architecture: ArchitectureCandidate) -> ArchitectureCandidate:
        """Optimize architecture for TPU deployment."""
        optimized_params = architecture.structure_params.copy()
        
        # TPU optimizations - dimensions should be multiples of 128
        dimension_params = ['hidden_size', 'intermediate_size', 'vector_dimension']
        
        for param in dimension_params:
            if param in optimized_params:
                value = optimized_params[param]
                optimized_params[param] = ((value + 127) // 128) * 128
        
        # Large batch sizes for TPU
        if 'batch_size' in optimized_params:
            optimized_params['batch_size'] = max(optimized_params['batch_size'], 128)
        
        optimized_params['tpu_optimized'] = True
        optimized_params['use_bfloat16'] = True  # bfloat16 for TPU
        
        optimized_architecture = ArchitectureCandidate(
            architecture_id=f"{architecture.architecture_id}_tpu_opt",
            architecture_type=architecture.architecture_type,
            structure_params=optimized_params,
            capabilities=architecture.capabilities,
            parent_architectures=[architecture.architecture_id]
        )
        
        return optimized_architecture
    
    def _optimize_for_edge(self, architecture: ArchitectureCandidate) -> ArchitectureCandidate:
        """Optimize architecture for edge device deployment."""
        optimized_params = architecture.structure_params.copy()
        
        # Edge device constraints - very small models
        scale_factor = 0.5
        
        for param in ['hidden_size', 'num_layers', 'vector_dimension']:
            if param in optimized_params:
                optimized_params[param] = max(1, int(optimized_params[param] * scale_factor))
        
        optimized_params['edge_optimized'] = True
        optimized_params['quantized'] = True  # INT8 quantization
        optimized_params['pruned'] = True  # Aggressive pruning
        
        optimized_architecture = ArchitectureCandidate(
            architecture_id=f"{architecture.architecture_id}_edge_opt",
            architecture_type=architecture.architecture_type,
            structure_params=optimized_params,
            capabilities=architecture.capabilities,
            parent_architectures=[architecture.architecture_id]
        )
        
        return optimized_architecture
    
    def _optimize_for_mobile(self, architecture: ArchitectureCandidate) -> ArchitectureCandidate:
        """Optimize architecture for mobile deployment."""
        return self._optimize_for_edge(architecture)  # Similar to edge optimization
    
    def _optimize_for_fpga(self, architecture: ArchitectureCandidate) -> ArchitectureCandidate:
        """Optimize architecture for FPGA deployment."""
        optimized_params = architecture.structure_params.copy()
        
        # FPGA optimizations - favor simpler operations
        if architecture.architecture_type == ArchitectureType.HDC_BASED:
            # HDC is well-suited for FPGA
            optimized_params['fpga_friendly'] = True
            optimized_params['parallel_operations'] = True
        else:
            # Simplify other architectures
            for param in ['num_layers', 'complexity']:
                if param in optimized_params and isinstance(optimized_params[param], int):
                    optimized_params[param] = max(1, int(optimized_params[param] * 0.7))
        
        optimized_params['fpga_optimized'] = True
        optimized_params['fixed_point'] = True  # Fixed-point arithmetic
        
        optimized_architecture = ArchitectureCandidate(
            architecture_id=f"{architecture.architecture_id}_fpga_opt",
            architecture_type=architecture.architecture_type,
            structure_params=optimized_params,
            capabilities=architecture.capabilities,
            parent_architectures=[architecture.architecture_id]
        )
        
        return optimized_architecture
    
    # Utility methods
    
    def _check_deployment_readiness(self, architecture: ArchitectureCandidate,
                                  constraints: Dict[str, Any]) -> bool:
        """Check if architecture is ready for deployment."""
        readiness_criteria = [
            architecture.accuracy_score >= constraints.get('min_accuracy', 0.8),
            architecture.efficiency_score >= constraints.get('min_efficiency', 0.6),
            architecture.historical_fidelity_score >= constraints.get('min_historical_fidelity', 0.7),
            architecture.model_size_mb <= constraints.get('max_model_size_mb', 100.0),
            architecture.inference_time_ms <= constraints.get('max_inference_time_ms', 500.0)
        ]
        
        return all(readiness_criteria)
    
    def _generate_optimization_id(self) -> str:
        """Generate unique optimization ID."""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        random_suffix = f"{random.randint(1000, 9999)}"
        return f"OPT_{timestamp}_{random_suffix}"
    
    def _generate_architecture_id(self) -> str:
        """Generate unique architecture ID."""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        random_suffix = f"{random.randint(100000, 999999)}"
        return f"ARCH_{timestamp}_{random_suffix}"
    
    def _update_optimization_stats(self, result: OptimizationResult):
        """Update optimization performance statistics."""
        self.optimization_stats['total_optimizations'] += 1
        
        if result.convergence_achieved and result.deployment_ready:
            self.optimization_stats['successful_optimizations'] += 1
        
        # Update average optimization time
        total_opts = self.optimization_stats['total_optimizations']
        current_avg = self.optimization_stats['average_optimization_time']
        new_avg = ((current_avg * (total_opts - 1)) + result.optimization_time) / total_opts
        self.optimization_stats['average_optimization_time'] = new_avg
        
        # Update compression operations count
        self.optimization_stats['compression_operations'] += len(result.compression_results)
        
        # Track best architectures
        if len(result.pareto_frontier) > 0:
            self.optimization_stats['best_architectures_found'] += len(result.pareto_frontier)
    
    # Public interface methods
    
    def get_optimization_history(self, limit: Optional[int] = None) -> List[OptimizationResult]:
        """Get optimization history."""
        history = sorted(self.optimization_history, key=lambda x: x.timestamp, reverse=True)
        
        if limit:
            history = history[:limit]
        
        return history
    
    def get_architecture_database(self) -> Dict[str, ArchitectureCandidate]:
        """Get architecture database."""
        return self.architecture_database.copy()
    
    def get_optimization_statistics(self) -> Dict[str, Any]:
        """Get optimization performance statistics."""
        stats = self.optimization_stats.copy()
        
        # Add derived metrics
        if stats['total_optimizations'] > 0:
            stats['success_rate'] = stats['successful_optimizations'] / stats['total_optimizations']
        
        # Add current system status
        stats['active_workers'] = self.max_workers
        stats['capability_count'] = len(self.capabilities_db)
        stats['architecture_database_size'] = len(self.architecture_database)
        
        return stats
    
    def export_optimization_report(self, optimization_ids: Optional[List[str]] = None) -> Dict[str, Any]:
        """Export comprehensive optimization report."""
        # Filter results if specific IDs provided
        if optimization_ids:
            results = [r for r in self.optimization_history if r.optimization_id in optimization_ids]
        else:
            results = self.optimization_history
        
        if not results:
            return {'error': 'No optimization results found'}
        
        # Generate report
        report = {
            'report_timestamp': datetime.now(),
            'optimization_count': len(results),
            'optimization_results': results,
            'summary_statistics': self._calculate_optimization_summary(results),
            'performance_statistics': self.get_optimization_statistics(),
            'pareto_analysis': self._analyze_pareto_frontiers(results),
            'compression_analysis': self._analyze_compression_results(results),
            'recommendations': self._generate_optimization_recommendations(results),
            'system_configuration': {
                'population_size': self.population_size,
                'max_generations': self.max_generations,
                'default_weights': self.default_weights
            }
        }
        
        return report
    
    def _calculate_optimization_summary(self, results: List[OptimizationResult]) -> Dict[str, Any]:
        """Calculate summary statistics for optimization results."""
        if not results:
            return {}
        
        best_scores = [r.best_architecture.pareto_score for r in results]
        optimization_times = [r.optimization_time for r in results]
        convergence_rates = [r.convergence_achieved for r in results]
        
        summary = {
            'average_best_score': np.mean(best_scores),
            'median_best_score': np.median(best_scores),
            'max_best_score': np.max(best_scores),
            'min_best_score': np.min(best_scores),
            'average_optimization_time': np.mean(optimization_times),
            'convergence_rate': np.mean(convergence_rates),
            'total_architectures_evaluated': sum(r.total_architectures_evaluated for r in results)
        }
        
        return summary
    
    def _analyze_pareto_frontiers(self, results: List[OptimizationResult]) -> Dict[str, Any]:
        """Analyze Pareto frontiers across optimizations."""
        all_frontier_architectures = []
        
        for result in results:
            all_frontier_architectures.extend(result.pareto_frontier)
        
        if not all_frontier_architectures:
            return {}
        
        # Analyze architecture types in frontiers
        arch_type_counts = {}
        for arch in all_frontier_architectures:
            arch_type = arch.architecture_type.value
            arch_type_counts[arch_type] = arch_type_counts.get(arch_type, 0) + 1
        
        # Analyze objective trade-offs
        accuracy_scores = [a.accuracy_score for a in all_frontier_architectures]
        efficiency_scores = [a.efficiency_score for a in all_frontier_architectures]
        fidelity_scores = [a.historical_fidelity_score for a in all_frontier_architectures]
        
        analysis = {
            'total_frontier_architectures': len(all_frontier_architectures),
            'architecture_type_distribution': arch_type_counts,
            'objective_ranges': {
                'accuracy': {'min': np.min(accuracy_scores), 'max': np.max(accuracy_scores)},
                'efficiency': {'min': np.min(efficiency_scores), 'max': np.max(efficiency_scores)},
                'historical_fidelity': {'min': np.min(fidelity_scores), 'max': np.max(fidelity_scores)}
            },
            'trade_off_correlation': {
                'accuracy_efficiency': np.corrcoef(accuracy_scores, efficiency_scores)[0, 1],
                'accuracy_fidelity': np.corrcoef(accuracy_scores, fidelity_scores)[0, 1],
                'efficiency_fidelity': np.corrcoef(efficiency_scores, fidelity_scores)[0, 1]
            }
        }
        
        return analysis
    
    def _analyze_compression_results(self, results: List[OptimizationResult]) -> Dict[str, Any]:
        """Analyze compression results across optimizations."""
        all_compression_results = []
        
        for result in results:
            all_compression_results.extend(result.compression_results)
        
        if not all_compression_results:
            return {}
        
        # Analyze compression methods
        method_counts = {}
        method_performance = {}
        
        for comp_result in all_compression_results:
            method = comp_result.compression_method.value
            method_counts[method] = method_counts.get(method, 0) + 1
            
            if method not in method_performance:
                method_performance[method] = {
                    'compression_ratios': [],
                    'accuracy_retentions': [],
                    'speed_improvements': []
                }
            
            method_performance[method]['compression_ratios'].append(comp_result.compression_ratio)
            method_performance[method]['accuracy_retentions'].append(comp_result.accuracy_retention)
            method_performance[method]['speed_improvements'].append(comp_result.speed_improvement)
        
        # Calculate averages
        method_averages = {}
        for method, perf in method_performance.items():
            method_averages[method] = {
                'avg_compression_ratio': np.mean(perf['compression_ratios']),
                'avg_accuracy_retention': np.mean(perf['accuracy_retentions']),
                'avg_speed_improvement': np.mean(perf['speed_improvements'])
            }
        
        analysis = {
            'total_compression_operations': len(all_compression_results),
            'compression_method_counts': method_counts,
            'compression_method_performance': method_averages,
            'overall_compression_ratio': np.mean([r.compression_ratio for r in all_compression_results]),
            'overall_accuracy_retention': np.mean([r.accuracy_retention for r in all_compression_results])
        }
        
        return analysis
    
    def _generate_optimization_recommendations(self, results: List[OptimizationResult]) -> List[str]:
        """Generate recommendations based on optimization results."""
        recommendations = []
        
        if not results:
            return recommendations
        
        # Analyze convergence rates
        convergence_rate = np.mean([r.convergence_achieved for r in results])
        if convergence_rate < 0.7:
            recommendations.append("Consider increasing population size or generations for better convergence")
        
        # Analyze best architecture types
        best_arch_types = [r.best_architecture.architecture_type.value for r in results]
        most_common_type = max(set(best_arch_types), key=best_arch_types.count)
        recommendations.append(f"Architecture type '{most_common_type}' shows best performance - consider focusing optimization efforts")
        
        # Analyze objective scores
        best_scores = [r.best_architecture.pareto_score for r in results]
        if np.mean(best_scores) < 0.8:
            recommendations.append("Overall Pareto scores are low - consider adjusting objective weights or constraints")
        
        # Analyze compression effectiveness
        compression_results = [r for r in results if r.compression_results]
        if len(compression_results) / len(results) < 0.5:
            recommendations.append("Consider applying compression more frequently for deployment readiness")
        
        return recommendations
    
    def shutdown(self):
        """Shutdown neural architecture optimizer."""
        self.process_pool.shutdown(wait=True)
        self.thread_pool.shutdown(wait=True)
        
        logger.info("NeuralArchitectureOptimizer shutdown completed")